A principal component analysis approach to correcting the knee flexion axis during gait. Issue 9 (14th June 2016)

• Record Type: Journal Article
• Authors: Jensen, Elisabeth; Lugade, Vipul; Crenshaw, Jeremy; Miller, Emily; Kaufman, Kenton
• Is Part Of: Journal of biomechanics. Volume 49:Issue 9(2016)
• Page Start: 1698
• Page End: 1704
• Publication Date: 2016-06-14
• Subjects: Gait analysis -- Kinematics -- Axis of rotation -- Marker placement -- Correction algorithm; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2016.03.046
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Accurate and precise knee flexion axis identification is critical for prescribing and assessing tibial and femoral derotation osteotomies, but is highly prone to marker misplacement-induced error. The purpose of this study was to develop an efficient algorithm for post-hoc correction of the knee flexion axis and test its efficacy relative to other established algorithms. Gait data were collected on twelve healthy subjects using standard marker placement as well as intentionally misplaced lateral knee markers. The efficacy of the algorithm was assessed by quantifying the reduction in knee angle errors. Crosstalk error was quantified from the coefficient of determination ( r 2 ) between knee flexion and adduction angles. Mean rotation offset error ( α o ) was quantified from the knee and hip rotation kinematics across the gait cycle. The principal component analysis (PCA)-based algorithm significantly reduced r 2 ( p <0.001) and caused α o, knee to converge toward 11.9±8.0° of external rotation, demonstrating improved certainty of the knee kinematics. The within-subject standard deviation of α o, hip between marker placements was reduced from 13.5±1.5° to 0.7±0.2° ( p <0.001), demonstrating improved precision of the knee kinematics. The PCA-based algorithm performed at levels comparable to a knee abduction–adduction minimization algorithm (Baker et al., 1999 ) and better than a null space algorithm (Schwartz and Rozumalski, 2005 ) for this healthy subject population.